Articoli correlati a Managing Uncertainty in Expert Systems
Sinossi
1 Introdution.- 1.1 Architecture of an Expert System.- 1.2 Building Expert Systems.- 1.2.1 Construction Stages.- 1.2.2 Expert System Tools.- 1.3 Uncertainty in Expert Systems.- 1.3.1 Sources of Uncertainty.- 1.3.2 Inference under Uncertainty.- 1.4 Taxonomy of Expert Systems.- 1.4.1 By Task Type.- 1.4.2 By User Interaction and Solution Status.- 2 Knowledge Representation.- 2.1 First-Order Logic.- 2.1.1 Propositional Calculus.- 2.1.2 Predicate Calculus.- 2.2 Production Systems.- 2.2.1 Production Rules.- 2.2.2 Data Base.- 2.2.3 Rule Interpreter.- 2.2.3.1 Pattern Matching.- 2.2.3.2 Conflict Resolution.- 2.2.4 Forward Chaining.- 2.2.5 Depth-First and Breadth-First Search.- 2.2.6 Backward Chaining.- 2.2.7 Metarules.- 2.2.8 Forward and Backward Reasoning Versus Chaining.- 2.2.9 Advantages and Disadvantagesof Production Systems.- 2.3 Semantic Nets.- 2.3.1 Basic Properties.- 2.3.2 Extended Semantic Nets.- 2.3.3 Concluding Remarks.- 2.4 Frames.- 2.4.1 Basic Concepts.- 2.4.2 Inference.- 2.4.3 Advantages and Disadvantages of Frame Systems.- Exercises.- 3 Knowledge Acquisition.- 3.1 Manual and Interactive Techniques.- 3.1.1 Interviewing.- 3.1.2 Observation.- 3.1.3 Multiple Experts.- 3.1.4 Psychology-Based Techniques.- 3.1.5 Knowledge Acquisition under Uncertainty.- 3.2 Machine Learning.- 3.3 Rule Learning from Examples.- 3.3.1 Decision Tables.- 3.3.1.1 A Single Decision Table.- 3.3.1.2 A Set of Decision Tables.- 3.3.2 Indiscernibility Relations and Partitions.- 3.3.2.1 Indiscernibility Relations.- 3.3.2.2 Partitions.- 3.3.3 Attribute Dependency and Rule Induction.- 3.3.3.1 Attribute Dependency Inequality.- 3.3.3.2 Equivalent Attribute Sets.- 3.3.3.3 Coverings.- 3.3.4 Checking Attribute Dependency.- 3.3.5 An Algorithm for Finding the Setof all Coverings.- 3.3.6 An Algorithm for Finding a Covering.- 3.3.7 Rule Induction from a Decision Table.- 3.3.7.1 Essential Attributes.- 3.3.7.2 Finding Rules from Coverings.- 3.3.8 Attribute Dependency and Data Bases.- 3.4 Rule Base Verification.- 3.4.1 Consistency.- 3.4.1.1 Redundancy.- 3.4.1.2 Conflict.- 3.4.1.3 Subsumption.- 3.4.1.4 Unnecessary Conditions.- 3.4.1.5 Circularity.- 3.4.2 Completeness.- 3.4.2.1 Unreferenced Attribute Values.- 3.4.2.2 Illegal Attribute and Decision Values.- 3.4.2.3 Unreachable Conditions.- 3.4.2.4 Unreachable Actions.- 3.4.2.5 Unreachable Goals.- 3.4.3 Concluding Remarks.- Exercises.- 4 One-Valued Quantitative Approaches.- 4.1 Probability Theory.- 4.1.1 Definition of a Probability.- 4.1.2 Kolmogorov's Axioms.- 4.1.3 Conditional Probability.- 4.1.4 Independent Events.- 4.1.5 Bayes' Rule.- 4.2 Systems using Bayes' Rule.- 4.2.1 Inference Network.- 4.2.2 Bayesian Updating.- 4.2.3 Uncertain Evidence.- 4.2.4 Multiple Evidences and Single Hypothesis.- 4.2.5 Multiple Evidences and Multiple Hypotheses.- 4.3 Belief Networks.- 4.3.1 Detection of Independences.- 4.3.2 Knowledge Updating.- 4.4 Certainty Factors.- 4.4.1 Basic Concepts.- 4.4.2 Propagation.- 4.4.2.1 Rules with Certain Single Conditions.- 4.4.2.2 Rules with Uncertain Single Conditions.- 4.4.2.3 Multiple Conditions.- 4.5 Concluding Remarks.- Exercises.- 5 Two-Valued Quantitative Approaches.- 5.1 Dempster-Shafer Theory.- 5.1.1 Frame of Discernment.- 5.1.2 Basic Probability Numbers.- 5.1.3 Belief Functions.- 5.1.4 Focal Elements and Core.- 5.1.5 Degrees of Doubt and Plausibility.- 5.1.6 Bayesian Belief Functions.- 5.1.7 Dempster's Rule of Combination.- 5.1.8 Support Functions.- 5.1.9 Combining Simple Support Functions.- 5.2 Inferno.- 5.2.1 Representation of Uncertainty.- 5.2.2 Relations.- 5.2.3 Propagation.- 5.2.4 Constraints.- 5.2.5 Termination of Propagation.- 5.2.6 Consistency of Information.- 5.3 Concluding Remarks.- Exercises.- 6 Set-Valued Quantitative Approaches.- 6.1 Fuzzy Set Theory.- 6.1.1 Fuzzy Sets and Operations.- 6.1.2 Extended Venn Diagrams.- 6.1.3 Fuzzy Relations and Operations.- 6.1.4 Possibility Theory and Possibility Distributions.- 6.1.5 Linguistic Variables, Linguistic Modifiers, and the Translation Mo
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